Glioma accounts for 80% of all malignant brain tumor and produces enormous clinical and public health burden due to its poor prognosis. Given the lack of known causes for glioma, understanding genetic susceptibility might provide new insights and opportunities for progress in unraveling the biological mechanisms behind this fatal cancer. Genome-wide association studies (GWAS) constitute a popular approach for investigating the association of single nucleotide polymorphisms (SNPs) with disease. On the other hand, studies examining how genetic variants modify gene expression in tissue (i.e., quantitative trait loci (eQTL) studies) focus on the molecular quantitative trait. As the traditionl GWAS analysis is subject to power loss due to its agnostic approach, new strategies are required to identify additional and scientifically meaningful susceptibility loci of glioma risk. Hre we propose to integrate eQTL studies to more powerfully test the SNP effect on disease in GWAS when eQTL studies and GWAS are conducted among different subjects. With a regression model for the joint effect of SNPs and gene expression on disease risk, we have developed an efficient testing procedure for the overall effect of an eQTL SNP set in a gene or a pathway and illustrated its utility in numerical simulation studies and an asthma study. We will pursue the integrated analysis with three specific study aims. In Aim 1, we will conduct eQTL analyses using genome-wide SNP and expression data collected from post- mortem brain tissue obtained from neurologically normal subjects. In Aim 2, we will first form eQTL SNP sets in a gene or an immunomodulating pathway identified from Aim 1 and then conduct eQTL SNP-set analyses to investigate the association of eQTL SNPs with the risk of glioma using the publicly available GWAS data of the glioma risk. In Aim 3, we will perform integrated analyses of the SNP-set of a gene identified in Aim 2 and its expression value to assess the gene-based effect on the risk of glioma, either through a direct effect of eQTL SNPs or an indirect effect mediated by gene expression. The goal of this project is to build a new framework of conducting and analyzing a GWAS and identify new susceptibility loci for glioma. As different genomic data (i.e., SNPs and gene expression) are integrated in the analysis, we would expect a more statistical power to detect the disease-driving susceptibility loci than the SNP-only analysis. Furthermore, the results from our eQTL-integrated approach will also be more biologically meaningful and interpretable than the conventional agnostic GWAS because eQTL SNPs are more likely to be functional and the eQTL effect on the disease risk is explicitly modeled.